import json
import os
import re
import unicodedata
from json.encoder import INFINITY
from typing import Any, Dict, List, Optional, Tuple, Union

import numpy as np
import regex

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, is_flax_available, is_tf_available, is_torch_available, logging
from ...utils.generic import _is_jax, _is_numpy


__a    :	str         =    logging.get_logger(__name__)

__a    :	str         =    {
    """artists_file""": """artists.json""",
    """lyrics_file""": """lyrics.json""",
    """genres_file""": """genres.json""",
}

__a    :	str         =    {
    """artists_file""": {
        """jukebox""": """https://huggingface.co/ArthurZ/jukebox/blob/main/artists.json""",
    },
    """genres_file""": {
        """jukebox""": """https://huggingface.co/ArthurZ/jukebox/blob/main/genres.json""",
    },
    """lyrics_file""": {
        """jukebox""": """https://huggingface.co/ArthurZ/jukebox/blob/main/lyrics.json""",
    },
}

__a    :	Tuple         =    {
    """jukebox""": 5_1_2,
}




class   _UpperCamelCase (		_UpperCAmelCase					):


			"""simple docstring"""






			__a    :					Any     					=		VOCAB_FILES_NAMES
			__a    :					int     					=		PRETRAINED_VOCAB_FILES_MAP
			__a    :					Optional[int]     					=		PRETRAINED_LYRIC_TOKENS_SIZES
			__a    :					Optional[int]     					=		['''input_ids''', '''attention_mask''']





			def __init__(  self ,  lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__=["v3", "v2", "v2"] ,  lowerCAmelCase__=5_12 ,  lowerCAmelCase__=5 ,  lowerCAmelCase__="<|endoftext|>" ,  **lowerCAmelCase__ ,  )	->   Optional[int]:

							'''simple docstring'''


							__lowercase					     =				AddedToken(lowerCAmelCase__ ,  lstrip=lowerCAmelCase__ ,  rstrip=lowerCAmelCase__		) if isinstance(lowerCAmelCase__ ,  lowerCAmelCase__		) else unk_token
							super().__init__(
							    unk_token=lowerCAmelCase__ ,  n_genres=lowerCAmelCase__ ,  version=lowerCAmelCase__ ,  max_n_lyric_tokens=lowerCAmelCase__ ,  **lowerCAmelCase__ ,  )
							__lowercase					     =				version
							__lowercase					     =				max_n_lyric_tokens
							__lowercase					     =				n_genres

							with open(lowerCAmelCase__ ,  encoding='''utf-8'''		) as vocab_handle:
											__lowercase					     =				json.load(lowerCAmelCase__		)

							with open(lowerCAmelCase__ ,  encoding='''utf-8'''		) as vocab_handle:
											__lowercase					     =				json.load(lowerCAmelCase__		)

							with open(lowerCAmelCase__ ,  encoding='''utf-8'''		) as vocab_handle:
											__lowercase					     =				json.load(lowerCAmelCase__		)

							__lowercase					     =				R'''[^A-Za-z0-9.,:;!?\-\'\"()\[\] \t\n]+'''
							# In v2, we had a n_vocab=80 and in v3 we missed + and so n_vocab=79 of characters.
							if len(self.lyrics_encoder		) == 79:
											__lowercase					     =				oov.replace(R'''\-\'''' ,  R'''\-+\''''		)

							__lowercase					     =				regex.compile(lowerCAmelCase__		)
							__lowercase					     =				{v: k for k, v in self.artists_encoder.items()}
							__lowercase					     =				{v: k for k, v in self.genres_encoder.items()}
							__lowercase					     =				{v: k for k, v in self.lyrics_encoder.items()}





			@property
			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   int:

							'''simple docstring'''


							return len(self.artists_encoder		) + len(self.genres_encoder		) + len(self.lyrics_encoder		)





			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   List[Any]:

							'''simple docstring'''


							return dict(self.artists_encoder ,  self.genres_encoder ,  self.lyrics_encoder		)





			def 			_SCREAMING_SNAKE_CASE     (  self ,  lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__		)	->   Optional[int]:

							'''simple docstring'''


							__lowercase					     =				[self.artists_encoder.get(lowerCAmelCase__ ,  0		) for artist in list_artists]
							for genres in range(len(lowerCAmelCase__		)		):
											__lowercase					     =				[self.genres_encoder.get(lowerCAmelCase__ ,  0		) for genre in list_genres[genres]]
											__lowercase					     =				list_genres[genres] + [-1] * (self.n_genres - len(list_genres[genres]		))

							__lowercase					     =				[[self.lyrics_encoder.get(lowerCAmelCase__ ,  0		) for character in list_lyrics[0]], [], []]
							return artists_id, list_genres, lyric_ids





			def 			_SCREAMING_SNAKE_CASE     (  self ,  lowerCAmelCase__		)	->   Union[str, Any]:

							'''simple docstring'''


							return list(lowerCAmelCase__		)





			def 			_SCREAMING_SNAKE_CASE     (  self ,  lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__ ,  **lowerCAmelCase__		)	->   Union[str, Any]:

							'''simple docstring'''


							__lowercase     ,	__lowercase     ,	__lowercase					     =				self.prepare_for_tokenization(lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__		)
							__lowercase					     =				self._tokenize(lowerCAmelCase__		)
							return artist, genre, lyrics





			def 			_SCREAMING_SNAKE_CASE     (  self ,  lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__ = False		)	->   Tuple[str, str, str, Dict[str, Any]]:

							'''simple docstring'''


							for idx in range(len(self.version		)		):
											if self.version[idx] == "v3":
															__lowercase					     =				artists[idx].lower()
															__lowercase					     =				[genres[idx].lower()]
											else:
															__lowercase					     =				self._normalize(artists[idx]		) + '''.v2'''
															__lowercase					     =				[
															    self._normalize(lowerCAmelCase__		) + '''.v2''' for genre in genres[idx].split('''_'''		)
															]  # split is for the full dictionary with combined genres

							if self.version[0] == "v2":
											__lowercase					     =				regex.compile(R'''[^A-Za-z0-9.,:;!?\-\'\"()\[\] \t\n]+'''		)
											__lowercase					     =				'''ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789.,:;!?-+\'\"()[] \t\n'''
											__lowercase					     =				{vocab[index]: index + 1 for index in range(len(lowerCAmelCase__		)		)}
											__lowercase					     =				0
											__lowercase					     =				len(lowerCAmelCase__		) + 1
											__lowercase					     =				self.vocab
											__lowercase					     =				{v: k for k, v in self.vocab.items()}
											__lowercase					     =				''''''
							else:
											__lowercase					     =				regex.compile(R'''[^A-Za-z0-9.,:;!?\-+\'\"()\[\] \t\n]+'''		)

							__lowercase					     =				self._run_strip_accents(lowerCAmelCase__		)
							__lowercase					     =				lyrics.replace('''\\''' ,  '''\n'''		)
							__lowercase					     =				self.out_of_vocab.sub('''''' ,  lowerCAmelCase__		), [], []
							return artists, genres, lyrics





			def 			_SCREAMING_SNAKE_CASE     (  self ,  lowerCAmelCase__		)	->   Optional[int]:

							'''simple docstring'''


							__lowercase					     =				unicodedata.normalize('''NFD''' ,  lowerCAmelCase__		)
							__lowercase					     =				[]
							for char in text:
											__lowercase					     =				unicodedata.category(lowerCAmelCase__		)
											if cat == "Mn":
															continue
											output.append(lowerCAmelCase__		)
							return "".join(lowerCAmelCase__		)





			def 			_SCREAMING_SNAKE_CASE     (  self ,  lowerCAmelCase__		)	->   str:

							'''simple docstring'''


							__lowercase					     =				(
							    [chr(lowerCAmelCase__		) for i in range(ord('''a'''		) ,  ord('''z'''		) + 1		)]
							    + [chr(lowerCAmelCase__		) for i in range(ord('''A'''		) ,  ord('''Z'''		) + 1		)]
							    + [chr(lowerCAmelCase__		) for i in range(ord('''0'''		) ,  ord('''9'''		) + 1		)]
							    + ['''.''']
							)
							__lowercase					     =				frozenset(lowerCAmelCase__		)
							__lowercase					     =				re.compile(R'''_+'''		)
							__lowercase					     =				''''''.join([c if c in accepted else '''_''' for c in text.lower()]		)
							__lowercase					     =				pattern.sub('''_''' ,  lowerCAmelCase__		).strip('''_'''		)
							return text





			def 			_SCREAMING_SNAKE_CASE     (  self ,  lowerCAmelCase__		)	->   str:

							'''simple docstring'''


							return " ".join(lowerCAmelCase__		)





			def 			_SCREAMING_SNAKE_CASE     (  self ,  lowerCAmelCase__ ,  lowerCAmelCase__ = None ,  lowerCAmelCase__ = False		)	->   int:

							'''simple docstring'''


							if not isinstance(lowerCAmelCase__ ,  lowerCAmelCase__		):
											__lowercase					     =				TensorType(lowerCAmelCase__		)

							# Get a function reference for the correct framework
							if tensor_type == TensorType.TENSORFLOW:
											if not is_tf_available():
															raise ImportError(
															    '''Unable to convert output to TensorFlow tensors format, TensorFlow is not installed.'''		)
											import tensorflow as tf

											__lowercase					     =				tf.constant
											__lowercase					     =				tf.is_tensor
							elif tensor_type == TensorType.PYTORCH:
											if not is_torch_available():
															raise ImportError('''Unable to convert output to PyTorch tensors format, PyTorch is not installed.'''		)
											import torch

											__lowercase					     =				torch.tensor
											__lowercase					     =				torch.is_tensor
							elif tensor_type == TensorType.JAX:
											if not is_flax_available():
															raise ImportError('''Unable to convert output to JAX tensors format, JAX is not installed.'''		)
											import jax.numpy as jnp  # noqa: F811

											__lowercase					     =				jnp.array
											__lowercase					     =				_is_jax
							else:
											__lowercase					     =				np.asarray
											__lowercase					     =				_is_numpy

							# Do the tensor conversion in batch

							try:
											if prepend_batch_axis:
															__lowercase					     =				[inputs]

											if not is_tensor(lowerCAmelCase__		):
															__lowercase					     =				as_tensor(lowerCAmelCase__		)
							except:  # noqa E722
											raise ValueError(
											    '''Unable to create tensor, you should probably activate truncation and/or padding '''
											    '''with \'padding=True\' \'truncation=True\' to have batched tensors with the same length.'''		)

							return inputs





			def __call__(  self ,  lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__="" ,  lowerCAmelCase__="pt"		)	->   BatchEncoding:

							'''simple docstring'''


							__lowercase					     =				[0, 0, 0]
							__lowercase					     =				[artist] * len(self.version		)
							__lowercase					     =				[genres] * len(self.version		)

							__lowercase     ,	__lowercase     ,	__lowercase					     =				self.tokenize(lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__		)
							__lowercase     ,	__lowercase     ,	__lowercase					     =				self._convert_token_to_id(lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__		)

							__lowercase					     =				[-INFINITY] * len(full_tokens[-1]		)
							__lowercase					     =				[
							    self.convert_to_tensors(
							        [input_ids + [artists_id[i]] + genres_ids[i] + full_tokens[i]] ,  tensor_type=lowerCAmelCase__		)
							    for i in range(len(self.version		)		)
							]
							return BatchEncoding({'''input_ids''': input_ids, '''attention_masks''': attention_masks}		)





			def 			_SCREAMING_SNAKE_CASE     (  self ,  lowerCAmelCase__ ,  lowerCAmelCase__ = None		)	->   Tuple[str]:

							'''simple docstring'''


							if not os.path.isdir(lowerCAmelCase__		):
											logger.error(F"Vocabulary path ({save_directory}) should be a directory"		)
											return

							__lowercase					     =				os.path.join(
							    lowerCAmelCase__ ,  (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''artists_file''']		)
							with open(lowerCAmelCase__ ,  '''w''' ,  encoding='''utf-8'''		) as f:
											f.write(json.dumps(self.artists_encoder ,  ensure_ascii=lowerCAmelCase__		)		)

							__lowercase					     =				os.path.join(
							    lowerCAmelCase__ ,  (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''genres_file''']		)
							with open(lowerCAmelCase__ ,  '''w''' ,  encoding='''utf-8'''		) as f:
											f.write(json.dumps(self.genres_encoder ,  ensure_ascii=lowerCAmelCase__		)		)

							__lowercase					     =				os.path.join(
							    lowerCAmelCase__ ,  (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''lyrics_file''']		)
							with open(lowerCAmelCase__ ,  '''w''' ,  encoding='''utf-8'''		) as f:
											f.write(json.dumps(self.lyrics_encoder ,  ensure_ascii=lowerCAmelCase__		)		)

							return (artists_file, genres_file, lyrics_file)





			def 			_SCREAMING_SNAKE_CASE     (  self ,  lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__		)	->   str:

							'''simple docstring'''


							__lowercase					     =				self.artists_decoder.get(lowerCAmelCase__		)
							__lowercase					     =				[self.genres_decoder.get(lowerCAmelCase__		) for genre in genres_index]
							__lowercase					     =				[self.lyrics_decoder.get(lowerCAmelCase__		) for character in lyric_index]
							return artist, genres, lyrics
import datasets


__a    :	Optional[Any]         =    """\
@InProceedings{conneau2018xnli,
  author = \"Conneau, Alexis
                 and Rinott, Ruty
                 and Lample, Guillaume
                 and Williams, Adina
                 and Bowman, Samuel R.
                 and Schwenk, Holger
                 and Stoyanov, Veselin\",
  title = \"XNLI: Evaluating Cross-lingual Sentence Representations\",
  booktitle = \"Proceedings of the 2018 Conference on Empirical Methods
               in Natural Language Processing\",
  year = \"2018\",
  publisher = \"Association for Computational Linguistics\",
  location = \"Brussels, Belgium\",
}
"""

__a    :	Optional[Any]         =    """\
XNLI is a subset of a few thousand examples from MNLI which has been translated
into a 14 different languages (some low-ish resource). As with MNLI, the goal is
to predict textual entailment (does sentence A imply/contradict/neither sentence
B) and is a classification task (given two sentences, predict one of three
labels).
"""

__a    :	str         =    """
Computes XNLI score which is just simple accuracy.
Args:
    predictions: Predicted labels.
    references: Ground truth labels.
Returns:
    'accuracy': accuracy
Examples:

    >>> predictions = [0, 1]
    >>> references = [0, 1]
    >>> xnli_metric = datasets.load_metric(\"xnli\")
    >>> results = xnli_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'accuracy': 1.0}
"""





def 							UpperCAmelCase		(       lowercase				,     lowercase				):







				"""simple docstring"""



				return (preds == labels).mean()




@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION		,_KWARGS_DESCRIPTION					)
class   _UpperCamelCase (		datasets.Metric					):


			"""simple docstring"""






			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   str:

							'''simple docstring'''


							return datasets.MetricInfo(
							    description=_DESCRIPTION ,  citation=_CITATION ,  inputs_description=_KWARGS_DESCRIPTION ,  features=datasets.Features(
							        {
							            '''predictions''': datasets.Value('''int64''' if self.config_name != '''sts-b''' else '''float32'''		),
							            '''references''': datasets.Value('''int64''' if self.config_name != '''sts-b''' else '''float32'''		),
							        }		) ,  codebase_urls=[] ,  reference_urls=[] ,  format='''numpy''' ,  )





			def 			_SCREAMING_SNAKE_CASE     (  self ,  lowerCAmelCase__ ,  lowerCAmelCase__		)	->   Dict:

							'''simple docstring'''


							return {"accuracy": simple_accuracy(lowerCAmelCase__ ,  lowerCAmelCase__		)}